Is there a moment where a galloping horse has all hooves off the ground?
Eadweard Muybridge, 1878. Public Domain
Chromosomes
Schematic representation of late-prophase chromosomes (1000-band stage) of man, chimpanzee, gorilla, and orangutan, arranged from left to right, respectively, to better visualize homology between the chromosomes of the great apes and the human complement.
Figure 2, Yunis and Prakash (1982)
What is this called?
[S]lice the data into parts according to one or more data dimensions, visualize each data slice separately, and then arrange the individual visualizations into a grid (Ch21, Wilke, 2019).
Called ‘small multiples’ (Tufte, 1991), ‘trellis plots’ (Becker et al., 1996), or ‘facet plots’ (Wickham, 2016).
Why do this?
Introduces a third dimension (first two dimensions being x and y). Essentially another aesthetic.
Tufte’s principles of graphical excellence:
Present many numbers in small space
Make large data sets coherent
What are guidelines?
Use when you want to focus audience attention on how differences
Faceting variable must be categorical
Each mini-plot should (normally) have same structure: common axes, scales, etc
Hadley Wickham (1979-)
New Zealand statistician, Chief Scientist at Posit PBC
Creator of ggplot2 and the tidyverse
John Chambers Award for Statistical Computing (2006); Fellow of ASA (2015); COPSS Presidents’ Award (2019)
# I've downloaded the data from Canvas into my project folder:malaria_countries <-read_csv("malaria_countries_long.csv")malaria_countries_ssafrica <- malaria_countries %>%filter(region =="Sub-Saharan Africa")
ggplot(malaria_countries_ssafrica) +geom_line(aes(x = year, y = incidence, group = country, color = country), alpha =0.75)
# To add labels to the plots, use a dataset with just one row per country:malaria_countries_ssafrica %>%group_by(country) %>%slice(1)
# Update p1 object from previous slide with the provided hintsp3 <- p2 +# The '.' means we are taking the data inherited from the p1 object# and further modifying itgeom_text(data = . %>%group_by(country) %>%slice(1),aes(label = country), x =2000, y =650, hjust =0, size =3)+theme(strip.text =element_blank())p3
Arrange countries by the change in incidence from 2000 to 2022
# Create a new variable called delta_incidence that is the difference # between the last and first incidence values for each country. # Then arrange the data by this variable and turn country into a factor# with levels in the order that they appear in the data:mutate(country =factor(country) %>%fct_inorder())
malaria_countries_ssafrica2 <- malaria_countries_ssafrica %>%group_by(country) %>%# Note we are using mutate not summarize since we want to keep the year-level# data. Because the data are grouped by `Country Code`, the result will just# be constant within countrymutate(delta_incidence =last(incidence) -first(incidence)) %>%ungroup() %>%# Now sort by the calculated value, then country name, then yeararrange(delta_incidence, country, year) %>%# Now we recharacterize Country name as a factor with levels as they# are arrangedmutate(country =factor(country) %>%fct_inorder())# update p3 object from previous slide with new data# Note use of new operator `%+%` for updating data. Type ?'%+%' for more inforp4 <- p3 %+% malaria_countries_ssafrica2p4
Anchor all panels with the Sub-Saharan-wide trajectory
# I've downloaded the data from Canvas into my project folder:malaria_all_ssafrica <-read_csv("Malaria/malaria_other_long.csv") %>%filter(name =="Sub-Saharan Africa")
ggplot(data = malaria_countries_ssafrica,mapping =aes(x = year, y = incidence)) +geom_line() +facet_wrap(vars(country), scales ="free_y") +scale_y_continuous(name ="Incidence per 1000 at-risk persons")
Think about differences between choice of varying y-axes: Varying y-axes are essentially many individual plots. Varying y-axes make it easier to evaluate differences within a panel but harder to compare between panels
Sort of a facet grid by categorized latitude and longtitude. The trend line provides an anchor. The amount of purple and orange allow to immediately determine
Code Together Task
No Spice: Make the standard faceted plot on slide 15;
Weak Sauce: Make the faceted plot without strips on slide 16; Make the faceted plot with varying y-axes on slide 20;
Medium Spice: Make the faceted plot with inside labels on slide 17
Yoga Flame: Make the faceted plot with reordered facets on slide 18
Dim Mak: Make the faceted plot with the Sub-Saharan Africa trajectory on slide 19
References
Becker, R.A., Cleveland, W.S. and Shyu, M.J., 1996. The visual design and control of trellis display. Journal of computational and Graphical Statistics, 5(2), pp.123-155.
